M2-Project
Mathias Flinta
21/10/2019
1 Introduction and set-up
This project is made to be read in html, so open the html file in your preferred webbrowser. As standard the code is hidden in this document, but you can show all by pressing the button “Code” in the top right of the document. You can also show individual chunks of code by pressing the buttons “Code” which are placed around in the document.
Link for google colab:
Link for github: https://github.com/DataEconomistDK/M2-Group-Assignment
In this project we will work with a dataset of 5.000 consumer reviews for a few Amazon electronic products like f. ex. Kindle. Data is collected between September 2017 and October 2018. This is a sample taken from Kaggle which is a part of a much bigger dataset available trough Datafiniti. The data can be collected from this link: https://www.kaggle.com/datafiniti/consumer-reviews-of-amazon-products?fbclid=IwAR1o_blPfHeBPmnUzAOW7Ct24L7fhbI3OGcbfaVgaDZENhVXwaCP4godKvQ#Datafiniti_Amazon_Consumer_Reviews_of_Amazon_Products.csv
Note there is 3 available dataset on kaggle, but the file used here is called “Datafiniti_Amazon_Consumer_Reviews_of_Amazon_Products”. The file is downloaded as is, and imported further below.
1.1 Loading packages
First i have some personal setup in my local R-Markdown on how i want to display warnings ect. And then i load my packages.
### Knitr options
knitr::opts_chunk$set(warning=FALSE,
message=FALSE,
fig.align="center"
)
options(warn=-1) # Hides all warnings, as the knitr options only work on local R-Markdown mode.
Sys.setenv(LANG = "en")# Packages
if (!require("pacman")) install.packages("pacman") # package for loading and checking packages :)
pacman::p_load(knitr, # For knitr to html
rmarkdown, # For formatting the document
tidyverse, # Standard datasciewnce toolkid (dplyr, ggplot2 et al.)
data.table, # for reading in data ect.
magrittr,# For advanced piping (%>% et al.)
igraph, # For network analysis
tidygraph, # For tidy-style graph manipulation
ggraph, # For ggplot2 style graph plotting
Matrix, # For some matrix functionality
ggforce, # Awesome plotting
kableExtra, # Formatting for tables
car, # recode functions
tidytext, # Structure text within tidyverse
topicmodels, # For topic modelling
tm, # text mining library
quanteda, # for LSA (latent semantic analysis)
uwot, # for UMAP
dbscan, # for density based clustering
SnowballC,
textdata,
wordcloud,
textstem, # for textstemming
tidyr,
widyr,
reshape2,
quanteda,
uwot,
dbscan,
plotly,
rsample,
glmnet,
doMC,
broom,
yardstick
)
# I set a seed for reproduciability
set.seed(123) # Have to be set every time a rng proces is being made. 1.2 Loading and filtering data
Now we load the data we downloaded from kaggle. From this file we select the following variables:
id: An id number given to each review created by us corrensponding to the row number of the raw data.
name: The full name of the product
reviews.rating: The rating of the product on a scale from 1-5.
reviews.title: The title of the review, given by the customer.
reviews.text: The review text written by the customer.
data_raw <- read_csv("Datafiniti_Amazon_Consumer_Reviews_of_Amazon_Products.csv") %>%
select(name, reviews.rating, reviews.text, reviews.title) %>%
mutate(id = row_number())As the data is very raw and messy we now do some cleaning. We remove everything that is not normal letters, such as punctuations, numbers, special characters ect, and changing all strings to lower case in the review text.
We will also do some lemmatization. The purpose of this is to not only analyze the exact word strings in the reviews, as this would include several possible forms of the words used. F. ex. think and thought. Instead we want to merge all possible forms of a word into it’s root word. Lemmatization try and do so, by using detailed dictionaries which the algorithm looks trough to link a given word string back to it’s root word. This is a more advanced method than stemming and should be beneficial in this report.
We here want to primarily work with tidy text, where there is one token per row. So new a clean and filtered dataset is created both with tokens and as normal dataframe with the review text.
tokens_clean <- data_raw %>%
unnest_tokens(word, reviews.text, to_lower = TRUE) %>%
mutate(word = word %>% str_remove_all("[^a-zA-Z]")) %>%
filter(str_length(word) > 0) %>%
mutate(word = lemmatize_words(word))
reviewtext_lemma <- tokens_clean %>%
group_by(id) %>%
summarize(reviews.text = str_c(word, collapse = " ")) %>%
ungroup() %>%
select(reviews.text) %>%
as_vector()
data_clean <- data_raw %>%
mutate(reviews.text = reviewtext_lemma)We now have 153.994 tokens, in their each seperate rows in the tokens dataset. By doing lemmatization the number of unique tokens are reduced from around 6000 to around 4600 words, which should prove quite beneficial.
2 Network analysis
In this assignment we want to use network analysis to gain new insights into how the reviews are structured. Here we extract bigrams from each review text, clean and prepare them to then create networks. Where we before considered tokens as individual words, we can create them as n-grams that are a consecutive sequence of words. Bigrams are n-grams with a length of 2 consecutive words. This can be used to gain context and connection between words.
Bigrams are now created, by unnesting the tokens.
bigrams <- data_clean %>%
unnest_tokens(bigram, reviews.text, token = "ngrams", n = 2) # n is the number of words to consider in each n-gram.
bigrams$bigram[1:2]## [1] "the display" "display be"Remember that each bigram overlap, as can be seen from above, so that the first token is “the display” and the second is “display is”. Now the most common bigrams are displayed.
#Counting common bigrams
bigrams %>%
count(bigram, sort = TRUE)Notice the most common bigrams are: “for my”, “easy to”, “to use”, “it is”. These are mostly stopwords, which is not very usefull for the analysis. To remove these from the bigrams, we now split the bigram into 2 columns word1 and word2, and then filter them away if either of them is a stopword. The stopwords are taken from a dictionary called stop_words. Now we make a new a new count to see the most bigrams after filtering.
bigrams_separated <- bigrams %>%
separate(bigram,c("word1","word2"),sep = " ")
bigrams_filtered <- bigrams_separated %>%
filter(!word1 %in% stop_words$word) %>%
filter(!word2 %in% stop_words$word)
#New bigram counts
bigram_counts <- bigrams_filtered %>%
count(word1, word2, sort = TRUE)
bigram_countsAbove we can see that the most common bigrams are now mostly product names such as “kindle fire”, “battery life”, “amazon fire”, “amazon echo” ect. We now combine the 2 columns again into a single column with the bigram, to do further analysis. This is done by using the ‘tidyr’ function ‘unite’. The purpose is to treat the bigram as a ‘term in a document’.
The interesting thing is now to visualize the relationship between all words. To this we will use the package igraph. Before doing this we will need to create the graph from a data frame of the bigrams. Where we take a data frame of edges with columns for ‘from’, ‘to’. Which is equal to n.
3 NLP
tokens_clean %>% count(word, sort=TRUE) %>% head(100)tokens_clean %<>%
anti_join(stop_words)own_stopwords <- tibble(word= c("im", "ive", "dont", "doesnt", "didnt"),
lexicon = "OWN")tokens_clean %<>%
anti_join(stop_words %>%
bind_rows(own_stopwords), by = "word") tokens_stemmed <- tokens_clean %<>%
add_count(id, word, name = "nword") %>%
add_count(id, name = "ntweet") %>%
filter(nword > 1 & ntweet > 1) %>%
select(-nword, -ntweet)tokens_stemmed <- tokens_stemmed %>%
mutate(word = wordStem(word))topwords <- tokens_stemmed %>% count(word, sort=TRUE)topwords %>%
top_n(20, n) %>%
ggplot(aes(x = word %>% fct_reorder(n), y = n)) +
geom_col() +
coord_flip() +
labs(title = "Word Counts",
x = "Frequency",
y = "Top Words")
topwords %>%
ggplot(aes(x = n)) +
geom_histogram()
wordcloud(topwords$word, topwords$n, random.order = FALSE,
max.words = 50, colors = brewer.pal(8,"Dark2"))
3.1 TF-IDF
We tried to run a tf-idf analysis but we couldn’t really say anything from the analysis, because there’s a lot of documents. If we were analyzing a number of books, the analyses may have made sence.
##Sentiment analysis
###Bing
sentiment_bing <- tokens_stemmed %>% inner_join(get_sentiments("bing"))
sentiment_bing %>% count(sentiment)sentiment_analysis <- sentiment_bing %>% filter(sentiment %in% c("positive"
, "negative"))
word_counts <- sentiment_analysis %>%
count(word, sentiment) %>%
group_by(sentiment) %>%
top_n(10, n) %>%
ungroup() %>%
mutate(
word2 = fct_reorder(word, n))
ggplot(word_counts, aes(x = word2, y = n, fill = sentiment)) +
geom_col(show.legend = FALSE) +
facet_wrap(~ sentiment, scales ="free") +
coord_flip() +
labs(title ="Sentiment Word Counts",x ="Words")
tokens_stemmed %>% inner_join(get_sentiments("bing")) %>% count(reviews.rating, sentiment)tokens_stemmed_bing = tokens_stemmed %>%
inner_join(get_sentiments("bing")) %>%
count(reviews.rating, sentiment) %>%
spread(sentiment, n) %>%
mutate(overall_sentiment = positive - negative)ggplot(
tokens_stemmed_bing,
aes(x = reviews.rating, y = overall_sentiment, fill = as.factor(reviews.rating))
) +
geom_col(show.legend = FALSE) +
coord_flip() +
labs(
title =
"Overall Sentiment by Stars"
,
subtitle =
"Reviews for Robotic Vacuums"
,
x =
"Stars"
,
y =
"Overall Sentiment"
)
bing_word_counts <- tokens_stemmed %>%
inner_join(get_sentiments("bing")) %>%
count(word, sentiment, sort = TRUE) %>%
ungroup()
bing_word_counts###Afinn
sentiment_afinnggplot(sentiment_afinn,aes(x = reviews.rating, y = sentiment,
fill = as.factor(reviews.rating))) +
geom_col(show.legend = FALSE) +
coord_flip() +
labs(title ="Overall Sentiment by Stars",subtitle ="Reviews for Robotic Vacuums",
x ="Stars",y ="Overall Sentiment")
labs(title ="Sentiment Word Counts",x ="Words")## $x
## [1] "Words"
##
## $title
## [1] "Sentiment Word Counts"
##
## attr(,"class")
## [1] "labels"##LSA
#Document-feature-matrix
data_dfm = tokens_stemmed %>% count(id, word) %>% cast_dfm(document = id, term = word, value = n)
data_dfm## Document-feature matrix of: 1,612 documents, 555 features (99.6% sparse).data_lsa_loading <- data_dfm$docs %>%
as.data.frame() %>%
rownames_to_column(var = "id") %>%
as_tibble()data_lsa_umap %<>% as.data.frame()data_lsa_hdbscan <- data_lsa_umap %>% as.matrix() %>% hdbscan(minPts = 50)x = data_lsa_umap %>%
bind_cols(cluster = data_lsa_hdbscan$cluster %>% as.factor(),
prob = data_lsa_hdbscan$membership_prob) %>%
ggplot(aes(x = V1, y = V2, col = cluster)) +
geom_point(aes(alpha = prob), shape = 21)
ggplotly(x)##LDA
pacman::p_load(lda,
topicmodels)data_dtm <- tokens_stemmed %>%
count(id, word) %>%
cast_dtm(document = id, term = word, value = n, weighting = tm::weightTf)
data_dtm## <<DocumentTermMatrix (documents: 1612, terms: 555)>>
## Non-/sparse entries: 3676/890984
## Sparsity : 100%
## Maximal term length: 11
## Weighting : term frequency (tf)data_dtm %>% removeSparseTerms(sparse = .99)## <<DocumentTermMatrix (documents: 1612, terms: 38)>>
## Non-/sparse entries: 2139/59117
## Sparsity : 97%
## Maximal term length: 7
## Weighting : term frequency (tf)data_dtm %>% removeSparseTerms(sparse = .999)## <<DocumentTermMatrix (documents: 1612, terms: 347)>>
## Non-/sparse entries: 3468/555896
## Sparsity : 99%
## Maximal term length: 11
## Weighting : term frequency (tf)data_dtm %>% removeSparseTerms(sparse = .9999)## <<DocumentTermMatrix (documents: 1612, terms: 555)>>
## Non-/sparse entries: 3676/890984
## Sparsity : 100%
## Maximal term length: 11
## Weighting : term frequency (tf)data_lda <- data_dtm %>%
LDA(k = 3, method = "Gibbs",
control = list(seed = 1337))lda_beta <- data_lda %>%
tidy(matrix = "beta") %>%
group_by(topic) %>%
arrange(topic, desc(beta)) %>%
slice(1:10) %>%
ungroup()
lda_beta %>% head()lda_beta %>%
mutate(term = reorder_within(term, beta, topic)) %>%
group_by(topic, term) %>%
arrange(desc(beta)) %>%
ungroup() %>%
ggplot(aes(term, beta, fill = as.factor(topic))) +
geom_col(show.legend = FALSE) +
coord_flip() +
scale_x_reordered() +
labs(title = "Top 10 terms in each LDA topic",
x = NULL, y = expression(beta)) +
facet_wrap(~ topic, ncol = 2, scales = "free")
It seems like cluster 1 contains some words with a tecnological character (tablet, screen, devic, app, game) while cluster 2 seems related to books and reading (kindl, read, book). The last cluster, 3, contains some positive words (love and smart) as the only cluster - besides “easi” in cluster 2
4 Machine learning
data_split <- data_clean %>%
select(id) %>%
initial_split()
train_data <- training(data_split)
test_data <- testing(data_split)Transforming training data to a sparse Matrix
sparse_words <- tokens_clean %>%
count(id,word) %>%
inner_join(train_data) %>%
cast_sparse(id,word)
class(sparse_words)## [1] "dgCMatrix"
## attr(,"package")
## [1] "Matrix"dim(sparse_words)## [1] 1199 481word_rownames <- as.integer(rownames(sparse_words))
data_joined <- data_frame(id = word_rownames) %>%
left_join(data_clean %>% select(id, reviews.rating))
rating_equal_5 <- data_joined$reviews.rating == "5"
model <-cv.glmnet(sparse_words,rating_equal_5, family="binomial",
parallel = TRUE, keep = TRUE)
plot(model)
plot(model$glmnet.fit)
coefs <- model$glmnet.fit %>%
tidy() %>%
filter(lambda == model$lambda.1se)
coefs %>%
group_by(estimate > 0) %>%
top_n(10, abs(estimate)) %>%
ungroup() %>%
ggplot(aes(fct_reorder(term, estimate),estimate, fill =estimate > 0)) +
geom_col(alpha = 0.8, show.legend = FALSE) +
coord_flip() +
labs(x = NULL, title = "SDS ER LIVET", subtitle = "OG I VED DET")
intercept <- coefs %>%
filter(term == "(Intercept)") %>%
pull(estimate)
classifications <- tokens_clean %>%
inner_join(test_data) %>%
inner_join(coefs, by = c("word" = "term")) %>%
group_by(id) %>%
summarize(score = sum(estimate)) %>%
mutate(probability = plogis(intercept + score))
classifications
2.1 Comment more about network
The plot above, show us some details about the structure of the text. We can for example see that such thing as ‘son’ ‘wife’ and ‘daugther’ are connected with ‘loves’.
#Correlation bigrams
Check up on this!
Maybe choose other names
Maybe add colour scale.